| 1. |
- Patel, Yashwant Singh, et al.
(författare)
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Modeling the green cloud continuum : integrating energy considerations into cloud-edge models
- 2024
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Ingår i: Cluster Computing. - : Springer. - 1386-7857 .- 1573-7543. ; 27:4, s. 4095-4125
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Tidskriftsartikel (refereegranskat)abstract
- The energy consumption of Cloud–Edge systems is becoming a critical concern economically, environmentally, and societally; some studies suggest data centers and networks will collectively consume 18% of global electrical power by 2030. New methods are needed to mitigate this consumption, e.g. energy-aware workload scheduling, improved usage of renewable energy sources, etc. These schemes need to understand the interaction between energy considerations and Cloud–Edge components. Model-based approaches are an effective way to do this; however, current theoretical Cloud–Edge models are limited, and few consider energy factors. This paper analyses all relevant models proposed between 2016 and 2023, discovers key omissions, and identifies the major energy considerations that need to be addressed for Green Cloud–Edge systems (including interaction with energy providers). We investigate how these can be integrated into existing and aggregated models, and conclude with the high-level architecture of our proposed solution to integrate energy and Cloud–Edge models together.
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| 2. |
- Banerjee, Sourasekhar, et al.
(författare)
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Towards post-disaster damage assessment using deep transfer learning and GAN-based data augmentation
- 2023
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Ingår i: ICDCN '23. - New York, NY, USA : ACM Digital Library. - 9781450397964 ; , s. 372-377
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Konferensbidrag (refereegranskat)abstract
- Cyber-physical disaster systems (CPDS) are a new cyber-physical application that collects physical realm measurements from IoT devices and sends them to the edge for damage severity analysis of impacted sites in the aftermath of a large-scale disaster. However, the lack of effective machine learning paradigms and the data and device heterogeneity of edge devices pose significant challenges in disaster damage assessment (DDA). To address these issues, we propose a generative adversarial network (GAN) and a lightweight, deep transfer learning-enabled, fine-tuned machine learning pipeline to reduce overall sensing error and improve the model's performance. In this paper, we applied several combinations of GANs (i.e., DCGAN, DiscoGAN, ProGAN, and Cycle-GAN) to generate fake images of the disaster. After that, three pre-trained models: VGG19, ResNet18, and DenseNet121, with deep transfer learning, are applied to classify the images of the disaster. We observed that the ResNet18 is the most pertinent model to achieve a test accuracy of 88.81%. With the experiments on real-world DDA applications, we have visualized the damage severity of disaster-impacted sites using different types of Class Activation Mapping (CAM) techniques, namely Grad-CAM++, Guided Grad-Cam, & Score-CAM. Finally, using k-means clustering, we have obtained the scatter plots to measure the damage severity into no damage, mild damage, and severe damage categories in the generated heat maps.
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| 3. |
- Patel, Yashwant Singh, et al.
(författare)
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Formal models for the energy-aware cloud-edge computing continuum : analysis and challenges
- 2023
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Ingår i: 2023 IEEE international conference on service-oriented system engineering (SOSE). - : IEEE. - 9798350322392 - 9798350322408 ; , s. 48-59
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Konferensbidrag (refereegranskat)abstract
- Cloud infrastructures are rapidly evolving from centralised systems to geographically distributed federations of edge devices, fog nodes, and clouds. These federations (often referred to as the Cloud-Edge Continuum) are the foundation upon which most modern digital systems depend, and consume enormous amounts of energy. This consumption is becoming a critical issue as society's energy challenges grow, and is a great concern for power grids which must balance the needs of clouds against other users. The Continuum is highly dynamic, mobile, and complex; new methods to improve energy efficiency must be based on formal scientific models that identify and take into account a huge range of heterogeneous components, interactions, stochastic properties, and (potentially contradictory) service-level agreements and stakeholder objectives. Currently, few formal models of federated Cloud-Edge systems exist - and none adequately represent and integrate energy considerations (e.g. multiple providers, renewable energy sources, pricing, and the need to balance consumption over large-areas with other non-Cloud consumers, etc.). This paper conducts a systematic analysis of current approaches to modelling Cloud, Cloud-Edge, and federated Continuum systems with an emphasis on the integration of energy considerations. We identify key omissions in the literature, and propose an initial high-level architecture and approach to begin addressing these - with the ultimate goal to develop a set of integrated models that include data centres, edge devices, fog nodes, energy providers, software workloads, end users, and stakeholder requirements and objectives. We conclude by highlighting the key research challenges that must be addressed to enable meaningful energy-aware Cloud-Edge Continuum modelling and simulation.
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